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http://dx.doi.org/10.9716/KITS.2019.18.1.187

A Study on Effective Sentiment Analysis through News Classification in Bankruptcy Prediction Model  

Kim, Chansong (한양대학교 일반대학원 비즈니스 인포매틱스학과)
Shin, Minsoo (한양대학교 경영학부)
Publication Information
Journal of Information Technology Services / v.18, no.1, 2019 , pp. 187-200 More about this Journal
Abstract
Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.
Keywords
Sentiment Analysis; Text Mining; Bankruptcy Prediction;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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